publication . Preprint . 2019

Non-Negative Kernel Sparse Coding for the Classification of Motion Data

Hosseini, Babak; Hülsmann, Felix; Botsch, Mario; Hammer, Barbara;
Open Access English
  • Published: 09 Mar 2019
Abstract
We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meani...
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Related Organizations
Download from

[5] H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa. Design of non-linear kernel dictionaries for object recognition. IEEE Transactions on Image Processing, 22(12):5123- 5135, 2013.

[6] L Shure. Brief history of nonnegative least squares in matlab. Blog available at: http://blogs. mathworks. com/loren, 2006.

[7] H Van Benthem Mark and R Keenan Michael. Fast algorithm for the solution of large-scale non-negativity-constrained least squares problems. Journal of Chemometrics, 18(10):441-450, 2004.

[8] Chih-Jen Lin. Projected gradient methods for nonnegative matrix factorization. Neural computation, 19(10):2756-2779, 2007.

[9] Amir Beck and Marc Teboulle. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. Society, 2(1):183-202, 2009.

[10] Zhuolin Jiang, Zhe Lin, and Larry S Davis. Label consistent k-svd: Learning a discriminative dictionary for recognition. IEEE TPAMI, 35(11):2651-2664, 2013.

[11] John Shawe-Taylor and Nello Cristianini. Kernel methods for pattern analysis. Cambridge university press, 2004. [OpenAIRE]

[12] Renchu Guan, Xiaohu Shi, Maurizio Marchese, Chen Yang, and Yanchun Liang. Text clustering with seeds affinity propagation. Knowledge and Data Engineering, IEEE Transactions on, 23(4):627-637, 2011.

[13] Bernhard Scholkopf, Alexander Smola, and Klaus-Robert Muller. Kernel principal component analysis. In International Conference on Artificial Neural Networks (ICANN), pages 583-588. Springer, 1997. [OpenAIRE]

[14] CMU. Carnegie mellon university graphics lab: http://mocap.cs.cmu.edu, Mar. 2007.

[15] M. H. Ko, G. W. West, S. Venkatesh, and M. Kumar. Online context recognition in multisensor systems using dynamic time warping. In ISSNIP'05, pages 283-288. IEEE, 2005.

[16] Jun Wang, Ashok Samal, and Jordan Green. Preliminary test of a real-time, interactive silent speech interface based on electromagnetic articulograph. In SLPAT'14, pages 38-45, 2014.

[17] Thomas Waltemate, Felix Hülsmann, Thies Pfeiffer, Stefan Kopp, and Mario Botsch. Realizing a low-latency virtual reality environment for motor learning. In VRST'15, pages 139-147. ACM, 2015.

Related research
Abstract
We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meani...
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Related Organizations
Download from

[5] H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa. Design of non-linear kernel dictionaries for object recognition. IEEE Transactions on Image Processing, 22(12):5123- 5135, 2013.

[6] L Shure. Brief history of nonnegative least squares in matlab. Blog available at: http://blogs. mathworks. com/loren, 2006.

[7] H Van Benthem Mark and R Keenan Michael. Fast algorithm for the solution of large-scale non-negativity-constrained least squares problems. Journal of Chemometrics, 18(10):441-450, 2004.

[8] Chih-Jen Lin. Projected gradient methods for nonnegative matrix factorization. Neural computation, 19(10):2756-2779, 2007.

[9] Amir Beck and Marc Teboulle. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. Society, 2(1):183-202, 2009.

[10] Zhuolin Jiang, Zhe Lin, and Larry S Davis. Label consistent k-svd: Learning a discriminative dictionary for recognition. IEEE TPAMI, 35(11):2651-2664, 2013.

[11] John Shawe-Taylor and Nello Cristianini. Kernel methods for pattern analysis. Cambridge university press, 2004. [OpenAIRE]

[12] Renchu Guan, Xiaohu Shi, Maurizio Marchese, Chen Yang, and Yanchun Liang. Text clustering with seeds affinity propagation. Knowledge and Data Engineering, IEEE Transactions on, 23(4):627-637, 2011.

[13] Bernhard Scholkopf, Alexander Smola, and Klaus-Robert Muller. Kernel principal component analysis. In International Conference on Artificial Neural Networks (ICANN), pages 583-588. Springer, 1997. [OpenAIRE]

[14] CMU. Carnegie mellon university graphics lab: http://mocap.cs.cmu.edu, Mar. 2007.

[15] M. H. Ko, G. W. West, S. Venkatesh, and M. Kumar. Online context recognition in multisensor systems using dynamic time warping. In ISSNIP'05, pages 283-288. IEEE, 2005.

[16] Jun Wang, Ashok Samal, and Jordan Green. Preliminary test of a real-time, interactive silent speech interface based on electromagnetic articulograph. In SLPAT'14, pages 38-45, 2014.

[17] Thomas Waltemate, Felix Hülsmann, Thies Pfeiffer, Stefan Kopp, and Mario Botsch. Realizing a low-latency virtual reality environment for motor learning. In VRST'15, pages 139-147. ACM, 2015.

Related research
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue